In [1]:
from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive
In [141]:
import os
import numpy as np
import pandas as pd
import zipfile
from PIL import Image
import matplotlib.pyplot as plt
import tensorflow
from tensorflow.keras.applications.mobilenet import MobileNet,  preprocess_input
from tensorflow.keras.layers import Concatenate, Conv2D, Reshape, UpSampling2D
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau
from tensorflow.keras.losses import binary_crossentropy
from tensorflow.keras.models import Model
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import ZeroPadding2D, Convolution2D, MaxPooling2D, Dropout, Flatten, Activation
from sklearn.model_selection import train_test_split, GridSearchCV
from tensorflow.keras.models import model_from_json
import cv2
from sklearn.svm import SVC
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.decomposition import PCA

Part 1. 1 a - Import and read ‘images.npy’

In [ ]:
data = np.load('/content/drive/MyDrive/Greatlearning/ComputerVsion Project/Images.npy', allow_pickle=True)
In [ ]:
data.shape
Out[ ]:
(393, 2)
In [ ]:
data[2][0]
Out[ ]:
array([[[  0,   0,   0, 255],
        [  0,   0,   0, 255],
        [  0,   0,   0, 255],
        ...,
        [  0,   0,   0, 255],
        [  0,   0,   0, 255],
        [  0,   0,   0, 255]],

       [[  0,   0,   0, 255],
        [  0,   0,   0, 255],
        [  0,   0,   0, 255],
        ...,
        [  0,   0,   0, 255],
        [  0,   0,   0, 255],
        [  0,   0,   0, 255]],

       [[  0,   0,   0, 255],
        [  0,   0,   0, 255],
        [  0,   0,   0, 255],
        ...,
        [  0,   0,   0, 255],
        [  0,   0,   0, 255],
        [  0,   0,   0, 255]],

       ...,

       [[ 80,  31,  18, 255],
        [ 82,  36,  23, 255],
        [ 79,  31,  21, 255],
        ...,
        [121, 114, 104, 255],
        [120, 112, 104, 255],
        [116, 109,  99, 255]],

       [[ 73,  38,  20, 255],
        [ 76,  40,  24, 255],
        [ 79,  45,  30, 255],
        ...,
        [121, 112, 104, 255],
        [117, 110, 102, 255],
        [113, 106,  97, 255]],

       [[ 79,  37,  21, 255],
        [ 76,  32,  16, 255],
        [ 77,  33,  18, 255],
        ...,
        [119, 108,  97, 255],
        [115, 106,  96, 255],
        [111, 103,  90, 255]]], dtype=uint8)
In [ ]:
data[2][1]
Out[ ]:
[{'label': ['Face'],
  'notes': '',
  'points': [{'x': 0.495, 'y': 0.27147766323024053},
   {'x': 0.595, 'y': 0.46048109965635736}],
  'imageWidth': 600,
  'imageHeight': 291}]

Part 1.1 b - Split the data into Features(X) & labels(Y). Unify shape of all the images.

In [ ]:
ALPHA = 1

IMAGE_HEIGHT = 224
IMAGE_WIDTH = 224

IMAGE_SIZE = 224
In [ ]:
#create masked image and preprocessed image (to feed mobile Net)
X = np.zeros((int(data.shape[0]), IMAGE_HEIGHT, IMAGE_WIDTH, 3))
y = np.zeros((int(data.shape[0]), IMAGE_WIDTH, IMAGE_HEIGHT))
for index in range(data.shape[0]):


    # img = cv2.resize(data[index][0], dsize=(IMAGE_HEIGHT, IMAGE_WIDTH), interpolation=cv2.INTER_CUBIC)
    # img = data[index][0].resize((IMAGE_SIZE, IMAGE_SIZE,3))
    img = data[index][0]
    # print(img.shape[2])
    if len(img.shape) >= 3:
      img = img[:, :, :3]

    # print(img.shape)
    num_channels = 1 if len(img.shape) == 2 else img.shape[-1]
    if num_channels == 1:
      # print("---")
      img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)

    # print(1 if len(img.shape) == 2 else img.shape[-1])
    img = cv2.resize(img, (224, 224))
    X[index] = preprocess_input(np.array(img, dtype=np.float32))


    for i in data[index][1]:
        x1 = int(i["points"][0]['x'] * IMAGE_WIDTH)
        x2 = int(i["points"][1]['x'] * IMAGE_WIDTH)
        y1 = int(i["points"][0]['y'] * IMAGE_HEIGHT)
        y2 = int(i["points"][1]['y'] * IMAGE_HEIGHT)
        y[index][y1:y2, x1:x2] = 1

Part 1.1 c - Split the data into train and test [400:9].

In [ ]:
train_size = 400 / (400 + 9) # (mentioned train : test is 400 : 9)
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=train_size, random_state=42)
In [ ]:
print("X train : ({},{})".format(len(X_train),len(X_train[0][0])))
print("y train : ({},{})".format(len(y_train),len(y_train[0])))
print("X test : ({},{})".format(len(X_test),1))
print("y test : ({},{})".format(len(y_test),len(y_test[0])))
# pd.DataFrame(X_train)

X train : (384,224)
y train : (384,224)
X test : (9,1)
y test : (9,224)
In [ ]:
len(X_train[0])
Out[ ]:
224
In [ ]:
print(X_train[0])

[[[-0.78823531 -0.83529413 -0.81960785]
  [-0.79607844 -0.85882354 -0.82745099]
  [-0.78823531 -0.85882354 -0.81960785]
  ...
  [-0.92941177 -0.97647059 -0.96078432]
  [-0.92941177 -0.97647059 -0.96078432]
  [-0.92941177 -0.97647059 -0.96078432]]

 [[-0.63137257 -0.7019608  -0.67843139]
  [-0.63921571 -0.71764708 -0.68627453]
  [-0.63137257 -0.71764708 -0.68627453]
  ...
  [-0.9137255  -0.96078432 -0.92941177]
  [-0.9137255  -0.96078432 -0.92941177]
  [-0.9137255  -0.96078432 -0.92941177]]

 [[-0.62352943 -0.71764708 -0.68627453]
  [-0.63137257 -0.72549021 -0.69411767]
  [-0.65490198 -0.74901962 -0.72549021]
  ...
  [-0.87450981 -0.95294118 -0.89803922]
  [-0.87450981 -0.95294118 -0.89803922]
  [-0.87450981 -0.95294118 -0.89803922]]

 ...

 [[-0.81176472 -0.85882354 -0.8509804 ]
  [-0.83529413 -0.84313726 -0.83529413]
  [-0.80392158 -0.81176472 -0.80392158]
  ...
  [-0.89803922 -0.99215686 -0.9137255 ]
  [-0.89803922 -0.99215686 -0.9137255 ]
  [-0.89803922 -0.99215686 -0.9137255 ]]

 [[-0.81176472 -0.8509804  -0.81960785]
  [-0.82745099 -0.84313726 -0.83529413]
  [-0.79607844 -0.82745099 -0.81960785]
  ...
  [-0.89803922 -0.99215686 -0.9137255 ]
  [-0.89803922 -0.99215686 -0.9137255 ]
  [-0.89803922 -0.99215686 -0.9137255 ]]

 [[-0.80392158 -0.8509804  -0.83529413]
  [-0.83529413 -0.84313726 -0.84313726]
  [-0.82745099 -0.84313726 -0.84313726]
  ...
  [-0.89803922 -0.99215686 -0.9137255 ]
  [-0.89803922 -0.99215686 -0.9137255 ]
  [-0.89803922 -0.99215686 -0.9137255 ]]]
In [ ]:
print(np.array(X_train).shape)

(384, 224, 224, 3)
In [ ]:
In [ ]:
print(np.array(y_train).shape)

(384, 224, 224)
In [ ]:
print()




























Part 1.1 d - Select random image from the train data and
display original image and masked image.



















In [ ]:



     


 



     



















In [ ]:



     


 



     



















In [ ]:



     


print("Image array:")
plt.imshow(X_train[20])



     






















    
    






WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).










    
    






Image array:










    
    
Out[ ]:








<matplotlib.image.AxesImage at 0x7f5f7e5c6500>










    
    































In [ ]:



     


print("Mased array:")
plt.imshow(y_train[20])



     






















    
    






Mased array:










    
    
Out[ ]:








<matplotlib.image.AxesImage at 0x7f5f191accd0>










    
    



































Part 1.2 a - Design a face mask detection model.



















In [ ]:



     


def create_model(trainable=True):
    model = MobileNet(input_shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3), include_top=False, alpha=ALPHA, weights="imagenet")

    for layer in model.layers:
        layer.trainable = trainable

    block1 = model.get_layer("conv_pw_5_relu").output
    block2 = model.get_layer("conv_pw_11_relu").output
    block3 = model.get_layer("conv_pw_13_relu").output

    x = Concatenate()([UpSampling2D()(block3), block2])
    x = Concatenate()([UpSampling2D()(x), block1])

    x = Conv2D(1, kernel_size=1, activation="sigmoid")(x)
    x = UpSampling2D(size=(8, 8))(x)  # Upsample by 8x8 to match the desired output shape

    return Model(inputs=model.input, outputs=x)



     























B. Design your own Dice Coefficient and Loss function. [2 Marks]



















In [ ]:



     


def dice_coefficient(y_true, y_pred):
    numerator = 2 * tensorflow.reduce_sum(y_true * y_pred)
    denominator = tensorflow.reduce_sum(y_true + y_pred)

    return numerator / (denominator + tensorflow.keras.backend.epsilon())

def loss(y_true, y_pred):
    return binary_crossentropy(y_true, y_pred) - tensorflow.keras.backend.log(dice_coefficient(y_true, y_pred) + tensorflow.keras.backend.epsilon())



     























C. Train and tune the model as required. [3 Marks]



















In [ ]:



     


model = create_model(False)
model.summary()



     






















    
    






Model: "model_1"
__________________________________________________________________________________________________
 Layer (type)                Output Shape                 Param #   Connected to                  
==================================================================================================
 input_4 (InputLayer)        [(None, 224, 224, 3)]        0         []                            
                                                                                                  
 conv1 (Conv2D)              (None, 112, 112, 32)         864       ['input_4[0][0]']             
                                                                                                  
 conv1_bn (BatchNormalizati  (None, 112, 112, 32)         128       ['conv1[0][0]']               
 on)                                                                                              
                                                                                                  
 conv1_relu (ReLU)           (None, 112, 112, 32)         0         ['conv1_bn[0][0]']            
                                                                                                  
 conv_dw_1 (DepthwiseConv2D  (None, 112, 112, 32)         288       ['conv1_relu[0][0]']          
 )                                                                                                
                                                                                                  
 conv_dw_1_bn (BatchNormali  (None, 112, 112, 32)         128       ['conv_dw_1[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_dw_1_relu (ReLU)       (None, 112, 112, 32)         0         ['conv_dw_1_bn[0][0]']        
                                                                                                  
 conv_pw_1 (Conv2D)          (None, 112, 112, 64)         2048      ['conv_dw_1_relu[0][0]']      
                                                                                                  
 conv_pw_1_bn (BatchNormali  (None, 112, 112, 64)         256       ['conv_pw_1[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_pw_1_relu (ReLU)       (None, 112, 112, 64)         0         ['conv_pw_1_bn[0][0]']        
                                                                                                  
 conv_pad_2 (ZeroPadding2D)  (None, 113, 113, 64)         0         ['conv_pw_1_relu[0][0]']      
                                                                                                  
 conv_dw_2 (DepthwiseConv2D  (None, 56, 56, 64)           576       ['conv_pad_2[0][0]']          
 )                                                                                                
                                                                                                  
 conv_dw_2_bn (BatchNormali  (None, 56, 56, 64)           256       ['conv_dw_2[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_dw_2_relu (ReLU)       (None, 56, 56, 64)           0         ['conv_dw_2_bn[0][0]']        
                                                                                                  
 conv_pw_2 (Conv2D)          (None, 56, 56, 128)          8192      ['conv_dw_2_relu[0][0]']      
                                                                                                  
 conv_pw_2_bn (BatchNormali  (None, 56, 56, 128)          512       ['conv_pw_2[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_pw_2_relu (ReLU)       (None, 56, 56, 128)          0         ['conv_pw_2_bn[0][0]']        
                                                                                                  
 conv_dw_3 (DepthwiseConv2D  (None, 56, 56, 128)          1152      ['conv_pw_2_relu[0][0]']      
 )                                                                                                
                                                                                                  
 conv_dw_3_bn (BatchNormali  (None, 56, 56, 128)          512       ['conv_dw_3[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_dw_3_relu (ReLU)       (None, 56, 56, 128)          0         ['conv_dw_3_bn[0][0]']        
                                                                                                  
 conv_pw_3 (Conv2D)          (None, 56, 56, 128)          16384     ['conv_dw_3_relu[0][0]']      
                                                                                                  
 conv_pw_3_bn (BatchNormali  (None, 56, 56, 128)          512       ['conv_pw_3[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_pw_3_relu (ReLU)       (None, 56, 56, 128)          0         ['conv_pw_3_bn[0][0]']        
                                                                                                  
 conv_pad_4 (ZeroPadding2D)  (None, 57, 57, 128)          0         ['conv_pw_3_relu[0][0]']      
                                                                                                  
 conv_dw_4 (DepthwiseConv2D  (None, 28, 28, 128)          1152      ['conv_pad_4[0][0]']          
 )                                                                                                
                                                                                                  
 conv_dw_4_bn (BatchNormali  (None, 28, 28, 128)          512       ['conv_dw_4[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_dw_4_relu (ReLU)       (None, 28, 28, 128)          0         ['conv_dw_4_bn[0][0]']        
                                                                                                  
 conv_pw_4 (Conv2D)          (None, 28, 28, 256)          32768     ['conv_dw_4_relu[0][0]']      
                                                                                                  
 conv_pw_4_bn (BatchNormali  (None, 28, 28, 256)          1024      ['conv_pw_4[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_pw_4_relu (ReLU)       (None, 28, 28, 256)          0         ['conv_pw_4_bn[0][0]']        
                                                                                                  
 conv_dw_5 (DepthwiseConv2D  (None, 28, 28, 256)          2304      ['conv_pw_4_relu[0][0]']      
 )                                                                                                
                                                                                                  
 conv_dw_5_bn (BatchNormali  (None, 28, 28, 256)          1024      ['conv_dw_5[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_dw_5_relu (ReLU)       (None, 28, 28, 256)          0         ['conv_dw_5_bn[0][0]']        
                                                                                                  
 conv_pw_5 (Conv2D)          (None, 28, 28, 256)          65536     ['conv_dw_5_relu[0][0]']      
                                                                                                  
 conv_pw_5_bn (BatchNormali  (None, 28, 28, 256)          1024      ['conv_pw_5[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_pw_5_relu (ReLU)       (None, 28, 28, 256)          0         ['conv_pw_5_bn[0][0]']        
                                                                                                  
 conv_pad_6 (ZeroPadding2D)  (None, 29, 29, 256)          0         ['conv_pw_5_relu[0][0]']      
                                                                                                  
 conv_dw_6 (DepthwiseConv2D  (None, 14, 14, 256)          2304      ['conv_pad_6[0][0]']          
 )                                                                                                
                                                                                                  
 conv_dw_6_bn (BatchNormali  (None, 14, 14, 256)          1024      ['conv_dw_6[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_dw_6_relu (ReLU)       (None, 14, 14, 256)          0         ['conv_dw_6_bn[0][0]']        
                                                                                                  
 conv_pw_6 (Conv2D)          (None, 14, 14, 512)          131072    ['conv_dw_6_relu[0][0]']      
                                                                                                  
 conv_pw_6_bn (BatchNormali  (None, 14, 14, 512)          2048      ['conv_pw_6[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_pw_6_relu (ReLU)       (None, 14, 14, 512)          0         ['conv_pw_6_bn[0][0]']        
                                                                                                  
 conv_dw_7 (DepthwiseConv2D  (None, 14, 14, 512)          4608      ['conv_pw_6_relu[0][0]']      
 )                                                                                                
                                                                                                  
 conv_dw_7_bn (BatchNormali  (None, 14, 14, 512)          2048      ['conv_dw_7[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_dw_7_relu (ReLU)       (None, 14, 14, 512)          0         ['conv_dw_7_bn[0][0]']        
                                                                                                  
 conv_pw_7 (Conv2D)          (None, 14, 14, 512)          262144    ['conv_dw_7_relu[0][0]']      
                                                                                                  
 conv_pw_7_bn (BatchNormali  (None, 14, 14, 512)          2048      ['conv_pw_7[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_pw_7_relu (ReLU)       (None, 14, 14, 512)          0         ['conv_pw_7_bn[0][0]']        
                                                                                                  
 conv_dw_8 (DepthwiseConv2D  (None, 14, 14, 512)          4608      ['conv_pw_7_relu[0][0]']      
 )                                                                                                
                                                                                                  
 conv_dw_8_bn (BatchNormali  (None, 14, 14, 512)          2048      ['conv_dw_8[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_dw_8_relu (ReLU)       (None, 14, 14, 512)          0         ['conv_dw_8_bn[0][0]']        
                                                                                                  
 conv_pw_8 (Conv2D)          (None, 14, 14, 512)          262144    ['conv_dw_8_relu[0][0]']      
                                                                                                  
 conv_pw_8_bn (BatchNormali  (None, 14, 14, 512)          2048      ['conv_pw_8[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_pw_8_relu (ReLU)       (None, 14, 14, 512)          0         ['conv_pw_8_bn[0][0]']        
                                                                                                  
 conv_dw_9 (DepthwiseConv2D  (None, 14, 14, 512)          4608      ['conv_pw_8_relu[0][0]']      
 )                                                                                                
                                                                                                  
 conv_dw_9_bn (BatchNormali  (None, 14, 14, 512)          2048      ['conv_dw_9[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_dw_9_relu (ReLU)       (None, 14, 14, 512)          0         ['conv_dw_9_bn[0][0]']        
                                                                                                  
 conv_pw_9 (Conv2D)          (None, 14, 14, 512)          262144    ['conv_dw_9_relu[0][0]']      
                                                                                                  
 conv_pw_9_bn (BatchNormali  (None, 14, 14, 512)          2048      ['conv_pw_9[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_pw_9_relu (ReLU)       (None, 14, 14, 512)          0         ['conv_pw_9_bn[0][0]']        
                                                                                                  
 conv_dw_10 (DepthwiseConv2  (None, 14, 14, 512)          4608      ['conv_pw_9_relu[0][0]']      
 D)                                                                                               
                                                                                                  
 conv_dw_10_bn (BatchNormal  (None, 14, 14, 512)          2048      ['conv_dw_10[0][0]']          
 ization)                                                                                         
                                                                                                  
 conv_dw_10_relu (ReLU)      (None, 14, 14, 512)          0         ['conv_dw_10_bn[0][0]']       
                                                                                                  
 conv_pw_10 (Conv2D)         (None, 14, 14, 512)          262144    ['conv_dw_10_relu[0][0]']     
                                                                                                  
 conv_pw_10_bn (BatchNormal  (None, 14, 14, 512)          2048      ['conv_pw_10[0][0]']          
 ization)                                                                                         
                                                                                                  
 conv_pw_10_relu (ReLU)      (None, 14, 14, 512)          0         ['conv_pw_10_bn[0][0]']       
                                                                                                  
 conv_dw_11 (DepthwiseConv2  (None, 14, 14, 512)          4608      ['conv_pw_10_relu[0][0]']     
 D)                                                                                               
                                                                                                  
 conv_dw_11_bn (BatchNormal  (None, 14, 14, 512)          2048      ['conv_dw_11[0][0]']          
 ization)                                                                                         
                                                                                                  
 conv_dw_11_relu (ReLU)      (None, 14, 14, 512)          0         ['conv_dw_11_bn[0][0]']       
                                                                                                  
 conv_pw_11 (Conv2D)         (None, 14, 14, 512)          262144    ['conv_dw_11_relu[0][0]']     
                                                                                                  
 conv_pw_11_bn (BatchNormal  (None, 14, 14, 512)          2048      ['conv_pw_11[0][0]']          
 ization)                                                                                         
                                                                                                  
 conv_pw_11_relu (ReLU)      (None, 14, 14, 512)          0         ['conv_pw_11_bn[0][0]']       
                                                                                                  
 conv_pad_12 (ZeroPadding2D  (None, 15, 15, 512)          0         ['conv_pw_11_relu[0][0]']     
 )                                                                                                
                                                                                                  
 conv_dw_12 (DepthwiseConv2  (None, 7, 7, 512)            4608      ['conv_pad_12[0][0]']         
 D)                                                                                               
                                                                                                  
 conv_dw_12_bn (BatchNormal  (None, 7, 7, 512)            2048      ['conv_dw_12[0][0]']          
 ization)                                                                                         
                                                                                                  
 conv_dw_12_relu (ReLU)      (None, 7, 7, 512)            0         ['conv_dw_12_bn[0][0]']       
                                                                                                  
 conv_pw_12 (Conv2D)         (None, 7, 7, 1024)           524288    ['conv_dw_12_relu[0][0]']     
                                                                                                  
 conv_pw_12_bn (BatchNormal  (None, 7, 7, 1024)           4096      ['conv_pw_12[0][0]']          
 ization)                                                                                         
                                                                                                  
 conv_pw_12_relu (ReLU)      (None, 7, 7, 1024)           0         ['conv_pw_12_bn[0][0]']       
                                                                                                  
 conv_dw_13 (DepthwiseConv2  (None, 7, 7, 1024)           9216      ['conv_pw_12_relu[0][0]']     
 D)                                                                                               
                                                                                                  
 conv_dw_13_bn (BatchNormal  (None, 7, 7, 1024)           4096      ['conv_dw_13[0][0]']          
 ization)                                                                                         
                                                                                                  
 conv_dw_13_relu (ReLU)      (None, 7, 7, 1024)           0         ['conv_dw_13_bn[0][0]']       
                                                                                                  
 conv_pw_13 (Conv2D)         (None, 7, 7, 1024)           1048576   ['conv_dw_13_relu[0][0]']     
                                                                                                  
 conv_pw_13_bn (BatchNormal  (None, 7, 7, 1024)           4096      ['conv_pw_13[0][0]']          
 ization)                                                                                         
                                                                                                  
 conv_pw_13_relu (ReLU)      (None, 7, 7, 1024)           0         ['conv_pw_13_bn[0][0]']       
                                                                                                  
 up_sampling2d_9 (UpSamplin  (None, 14, 14, 1024)         0         ['conv_pw_13_relu[0][0]']     
 g2D)                                                                                             
                                                                                                  
 concatenate_8 (Concatenate  (None, 14, 14, 1536)         0         ['up_sampling2d_9[0][0]',     
 )                                                                   'conv_pw_11_relu[0][0]']     
                                                                                                  
 up_sampling2d_10 (UpSampli  (None, 28, 28, 1536)         0         ['concatenate_8[0][0]']       
 ng2D)                                                                                            
                                                                                                  
 concatenate_9 (Concatenate  (None, 28, 28, 1792)         0         ['up_sampling2d_10[0][0]',    
 )                                                                   'conv_pw_5_relu[0][0]']      
                                                                                                  
 conv2d_3 (Conv2D)           (None, 28, 28, 1)            1793      ['concatenate_9[0][0]']       
                                                                                                  
 up_sampling2d_11 (UpSampli  (None, 224, 224, 1)          0         ['conv2d_3[0][0]']            
 ng2D)                                                                                            
                                                                                                  
==================================================================================================
Total params: 3230657 (12.32 MB)
Trainable params: 1793 (7.00 KB)
Non-trainable params: 3228864 (12.32 MB)
__________________________________________________________________________________________________
























In [ ]:



     


model.compile(loss=loss, optimizer='adam', metrics=[dice_coefficient])



     








































In [ ]:



     


checkpoint = ModelCheckpoint("model-{val_loss:.2f}.h5", monitor="val_loss", verbose=1, save_best_only=True, save_weights_only=True)

stop = EarlyStopping(monitor="val_loss", patience=5)

reduce_lr = ReduceLROnPlateau(monitor="val_loss", factor=0.2, patience=5, min_lr=1e-6, verbose=1)



     



















In [ ]:



     


tensorflow.keras.backend.clear_session()
tensorflow.config.run_functions_eagerly(True)
model.fit(X_train, y_train, validation_data = (X_test, y_test), epochs=50, batch_size=1,callbacks=[checkpoint, reduce_lr, stop])



     






















    
    






/usr/local/lib/python3.10/dist-packages/tensorflow/python/data/ops/structured_function.py:258: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.
  warnings.warn(










    
    






Epoch 1/50
384/384 [==============================] - ETA: 0s - loss: 1.1251 - dice_coefficient: 0.4877
Epoch 1: val_loss improved from inf to 1.14322, saving model to model-1.14.h5
384/384 [==============================] - 50s 130ms/step - loss: 1.1251 - dice_coefficient: 0.4877 - val_loss: 1.1432 - val_dice_coefficient: 0.4809 - lr: 0.0010
Epoch 2/50
384/384 [==============================] - ETA: 0s - loss: 0.8327 - dice_coefficient: 0.5844
Epoch 2: val_loss improved from 1.14322 to 1.10039, saving model to model-1.10.h5
384/384 [==============================] - 49s 127ms/step - loss: 0.8327 - dice_coefficient: 0.5844 - val_loss: 1.1004 - val_dice_coefficient: 0.5024 - lr: 0.0010
Epoch 3/50
384/384 [==============================] - ETA: 0s - loss: 0.7817 - dice_coefficient: 0.6018
Epoch 3: val_loss improved from 1.10039 to 1.06535, saving model to model-1.07.h5
384/384 [==============================] - 50s 129ms/step - loss: 0.7817 - dice_coefficient: 0.6018 - val_loss: 1.0654 - val_dice_coefficient: 0.5148 - lr: 0.0010
Epoch 4/50
384/384 [==============================] - ETA: 0s - loss: 0.7545 - dice_coefficient: 0.6126
Epoch 4: val_loss did not improve from 1.06535
384/384 [==============================] - 51s 133ms/step - loss: 0.7545 - dice_coefficient: 0.6126 - val_loss: 1.1505 - val_dice_coefficient: 0.5113 - lr: 0.0010
Epoch 5/50
384/384 [==============================] - ETA: 0s - loss: 0.7408 - dice_coefficient: 0.6179
Epoch 5: val_loss did not improve from 1.06535
384/384 [==============================] - 51s 133ms/step - loss: 0.7408 - dice_coefficient: 0.6179 - val_loss: 1.0734 - val_dice_coefficient: 0.5157 - lr: 0.0010
Epoch 6/50
384/384 [==============================] - ETA: 0s - loss: 0.7351 - dice_coefficient: 0.6221
Epoch 6: val_loss improved from 1.06535 to 1.04633, saving model to model-1.05.h5
384/384 [==============================] - 49s 128ms/step - loss: 0.7351 - dice_coefficient: 0.6221 - val_loss: 1.0463 - val_dice_coefficient: 0.5259 - lr: 0.0010
Epoch 7/50
384/384 [==============================] - ETA: 0s - loss: 0.7199 - dice_coefficient: 0.6283
Epoch 7: val_loss did not improve from 1.04633
384/384 [==============================] - 52s 135ms/step - loss: 0.7199 - dice_coefficient: 0.6283 - val_loss: 1.2773 - val_dice_coefficient: 0.5046 - lr: 0.0010
Epoch 8/50
384/384 [==============================] - ETA: 0s - loss: 0.7068 - dice_coefficient: 0.6317
Epoch 8: val_loss did not improve from 1.04633
384/384 [==============================] - 49s 127ms/step - loss: 0.7068 - dice_coefficient: 0.6317 - val_loss: 1.0678 - val_dice_coefficient: 0.5264 - lr: 0.0010
Epoch 9/50
384/384 [==============================] - ETA: 0s - loss: 0.7010 - dice_coefficient: 0.6360
Epoch 9: val_loss did not improve from 1.04633
384/384 [==============================] - 49s 128ms/step - loss: 0.7010 - dice_coefficient: 0.6360 - val_loss: 1.0736 - val_dice_coefficient: 0.5230 - lr: 0.0010
Epoch 10/50
384/384 [==============================] - ETA: 0s - loss: 0.6927 - dice_coefficient: 0.6396
Epoch 10: val_loss did not improve from 1.04633
384/384 [==============================] - 51s 132ms/step - loss: 0.6927 - dice_coefficient: 0.6396 - val_loss: 1.0514 - val_dice_coefficient: 0.5284 - lr: 0.0010
Epoch 11/50
384/384 [==============================] - ETA: 0s - loss: 0.6777 - dice_coefficient: 0.6436
Epoch 11: val_loss did not improve from 1.04633

Epoch 11: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.
384/384 [==============================] - 50s 130ms/step - loss: 0.6777 - dice_coefficient: 0.6436 - val_loss: 1.1399 - val_dice_coefficient: 0.5191 - lr: 0.0010










    
    
Out[ ]:








<keras.src.callbacks.History at 0x7f5e6a0602b0>




























Part 1.2 d - Evaluate and share insights on
performance of the model.



















In [ ]:



     


print("Test image:")
plt.imshow(X_test[2])



     






















    
    






Test image:










    
    






WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).










    
    
Out[ ]:








<matplotlib.image.AxesImage at 0x7f5e6a2af820>










    
    































In [ ]:



     


print("Test masked image")
plt.imshow(y_test[2])



     






















    
    






Test masked image










    
    
Out[ ]:








<matplotlib.image.AxesImage at 0x7f5e6fc5bcd0>










    
    































In [ ]:



     


 



     



















In [ ]:



     


pred_mask = cv2.resize(1.0 * (model.predict(x=np.array([X_test[2]]))[0] > 0.5), (IMAGE_WIDTH, IMAGE_HEIGHT))
plt.imshow(X_test[2])
plt.imshow(pred_mask, alpha=0.9)



     






















    
    






1/1 [==============================] - 0s 98ms/step










    
    






WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).










    
    
Out[ ]:








<matplotlib.image.AxesImage at 0x7f5e6761bfd0>










    
    































In [ ]:



     


# Parameters
num_samples = len(X_test)
num_columns = 3
image_scale = 3
num_rows = np.ceil(num_samples / (num_columns // 3)).astype(int)

# Create the figure
plt.figure(figsize=(image_scale * num_columns, image_scale * num_rows))

for i in range(num_samples):
    row = i // (num_columns // 3)
    col = i % (num_columns // 3) * 3

    plt.subplot(num_rows, num_columns, col + 1 + row * num_columns)
    plt.imshow(X_test[i])
    plt.title(f"Image {i + 1}")
    plt.axis('off')

    plt.subplot(num_rows, num_columns, col + 2 + row * num_columns)
    plt.imshow(y_test[i], cmap='gray')
    plt.title(f"Mask {i + 1}")
    plt.axis('off')

    pred_mask = cv2.resize(1.0 * (model.predict(x=np.array([X_test[i]]))[0] > 0.5), (IMAGE_WIDTH, IMAGE_HEIGHT))
    plt.subplot(num_rows, num_columns, col + 3 + row * num_columns)
    plt.imshow(X_test[i])
    plt.imshow(pred_mask, alpha=0.9)
    plt.title(f"Predicted mask {i + 1}")
    plt.axis('off')

plt.tight_layout()
plt.show()



     






















    
    






WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).










    
    






1/1 [==============================] - 0s 68ms/step










    
    






WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).










    
    






1/1 [==============================] - 0s 66ms/step










    
    






WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).










    
    






1/1 [==============================] - 0s 72ms/step










    
    






WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).










    
    






1/1 [==============================] - 0s 67ms/step










    
    






WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).










    
    






1/1 [==============================] - 0s 72ms/step










    
    






WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).










    
    






1/1 [==============================] - 0s 67ms/step










    
    






WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).










    
    






1/1 [==============================] - 0s 66ms/step










    
    






WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).










    
    






1/1 [==============================] - 0s 71ms/step










    
    






WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).










    
    






1/1 [==============================] - 0s 73ms/step










    
    






WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).










    
    



































Insights


The prediction was very good, most of the faces was predicted and segmented/masked in the predicted image
Used early stopping to overcome overfitting, save resources etc..


dice_coefficient: 0.6436


val_dice_coefficient: 0.5191


Variations in the training process's loss and dice coefficient performance point to possible problems with convergence and model stability. Though there are significant swings, the validation loss appears to be steadily declining over epochs, suggesting improvement.























Part 1.3 - Test the model predictions on the test
image: ‘image with index 3 in the test data’ and
visualise the predicted masks on the faces in the
image.



















In [ ]:



     


print("Test image: (indx 3)")
plt.imshow(X_test[3])



     






















    
    






WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).










    
    






Test image: (indx 3)










    
    
Out[ ]:








<matplotlib.image.AxesImage at 0x7f5e6ffe7a90>










    
    































In [ ]:



     


print("Test masked image: (indx 3)")
plt.imshow(y_test[3])



     






















    
    






Test masked image: (indx 3)










    
    
Out[ ]:








<matplotlib.image.AxesImage at 0x7f5e6f8bd210>










    
    































In [ ]:



     


pred_mask = cv2.resize(1.0 * (model.predict(x=np.array([X_test[3]]))[0] > 0.5), (IMAGE_WIDTH, IMAGE_HEIGHT))
plt.imshow(X_test[3])
plt.imshow(pred_mask, alpha=0.9)



     






















    
    






1/1 [==============================] - 0s 71ms/step










    
    






WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).










    
    
Out[ ]:








<matplotlib.image.AxesImage at 0x7f5e921bc5e0>










    
    



































Part 2.1 a Read/import images from folder
‘training_images’.



















In [ ]:



     


folder_path = '/content/drive/MyDrive/Greatlearning/ComputerVsion Project/training_images'

images = []
filenames = []

for filename in os.listdir(folder_path):
    if filename.endswith('.jpg') or filename.endswith('.png'):
        img_path = os.path.join(folder_path, filename)
        img = cv2.imread(img_path)
        images.append(img)
        filenames.append(filename)



     



















In [ ]:



     


images[0]



     






















    
    
Out[ ]:







      
ndarray (600, 600, 3) 
array([[[197, 199, 207],
        [197, 200, 205],
        [198, 198, 204],
        ...,
        [220, 216, 221],
        [220, 216, 221],
        [220, 216, 221]],

       [[201, 201, 207],
        [198, 198, 204],
        [197, 197, 203],
        ...,
        [220, 216, 221],
        [220, 216, 221],
        [220, 216, 221]],

       [[204, 202, 208],
        [200, 198, 204],
        [199, 197, 203],
        ...,
        [220, 216, 221],
        [220, 216, 221],
        [220, 216, 221]],

       ...,

       [[216, 215, 219],
        [216, 215, 219],
        [217, 216, 220],
        ...,
        [141, 235, 248],
        [141, 235, 248],
        [141, 235, 248]],

       [[216, 215, 219],
        [216, 215, 219],
        [216, 215, 219],
        ...,
        [141, 235, 248],
        [141, 235, 248],
        [141, 235, 248]],

       [[215, 214, 218],
        [215, 214, 218],
        [216, 215, 219],
        ...,
        [141, 235, 248],
        [141, 235, 248],
        [141, 235, 248]]], dtype=uint8)
























In [ ]:



     


filenames[0]



     






















    
    
Out[ ]:








'real_00088.jpg'




























Part 2.1 b - Write a loop which will iterate
through all the images in the
‘training_images’ folder and detect the
faces present on all the images.



















In [ ]:



     


face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

face_metadata = []

for filename in os.listdir(folder_path):
    if filename.endswith('.jpg') or filename.endswith('.png'):
        img_path = os.path.join(folder_path, filename)
        img = cv2.imread(img_path)
        gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

        faces = face_cascade.detectMultiScale(gray_img, scaleFactor=1.1, minNeighbors=5)
        for (x, y, w, h) in faces:
            face_metadata.append({ 'x': x, 'y': y, 'width': w, 'height': h , 'Total_Faces' : len(faces),'Image_Name': filename,})

print("Face detection completed")



     






















    
    






Face detection completed




























Part 2.1 c - From the same loop above, extract
metadata of the faces and write into a DataFrame.



















In [ ]:



     


df = pd.DataFrame(face_metadata)
df.head()



     






















    
    
Out[ ]:







  

    




  
    

      
      x
      y
      width
      height
      Total_Faces
      Image_Name
    

  
  
    

      0
      108
      129
      447
      447
      1
      real_00088.jpg
    

    

      1
      126
      241
      343
      343
      1
      real_00083.jpg
    

    

      2
      67
      56
      461
      461
      1
      real_00076.jpg
    

    

      3
      40
      138
      408
      408
      1
      real_00079.jpg
    

    

      4
      131
      129
      438
      438
      1
      real_00080.jpg
    

  




    


  

    

  

    
  





  



  



    

  





























Already extracted the metadata of faces in the same loop for the above qn























Part 2.1 d - Save the output Dataframe in .csv
format..



















In [ ]:



     


df.to_csv('face_metadata.csv', index=False)



     























Part 2.1 a - Unzip, read and Load data(‘PINS.zip’)
into session.



















In [ ]:



     


zip_file_path = '/content/drive/MyDrive/Greatlearning/ComputerVsion Project/PINS.zip'

extract_dir = '/content/drive/MyDrive/Greatlearning/ComputerVsion Project/PINS'

if not os.path.exists(extract_dir):
    os.makedirs(extract_dir)

with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
    zip_ref.extractall(extract_dir)

extracted_files = os.listdir(extract_dir)
print("Files extracted successfully:", extracted_files)



     






















    
    






Files extracted successfully: ['PINS', '__MACOSX']




























Part 2.1 b - Write function to create
metadata of the image.



















In [4]:



     


class IdentityMetadata():
    def __init__(self, base, name, file):
        # print(base, name, file)
        # dataset base directory
        self.base = base
        # identity name
        self.name = name
        # image file name
        self.file = file

    def __repr__(self):
        return self.image_path()

    def image_path(self):
        return os.path.join(self.base, self.name, self.file)

def load_metadata(path):
    metadata = []
    for i in os.listdir(path):
        for f in os.listdir(os.path.join(path, i)):
            # Check file extension. Allow only jpg/jpeg' files.
            ext = os.path.splitext(f)[1]
            if ext == '.jpg' or ext == '.jpeg':
                metadata.append(IdentityMetadata(path, i, f))
    return np.array(metadata)



     























Part 2.1 c - Write a loop to iterate through each and
every image and create metadata for all the
images.



















In [37]:



     


metadata = load_metadata('/content/drive/MyDrive/Greatlearning/ComputerVsion Project/PINS/PINS')



     



















In [6]:



     


print(metadata)



     






















    
    






[/content/drive/MyDrive/Greatlearning/ComputerVsion Project/PINS/PINS/pins_mark zuckerberg/mark zuckerberg1.jpg
 /content/drive/MyDrive/Greatlearning/ComputerVsion Project/PINS/PINS/pins_mark zuckerberg/mark zuckerberg50.jpg
 /content/drive/MyDrive/Greatlearning/ComputerVsion Project/PINS/PINS/pins_mark zuckerberg/mark zuckerberg44.jpg
 ...
 /content/drive/MyDrive/Greatlearning/ComputerVsion Project/PINS/PINS/pins_Melissa benoit/Melissa benoit45.jpg
 /content/drive/MyDrive/Greatlearning/ComputerVsion Project/PINS/PINS/pins_Melissa benoit/Melissa benoit53.jpg
 /content/drive/MyDrive/Greatlearning/ComputerVsion Project/PINS/PINS/pins_Melissa benoit/Melissa benoit47.jpg]
























In [7]:



     


def load_image(path):
    img = cv2.imread(path, 1)
    return img[...,::-1]



     



















In [8]:



     


load_image(metadata[0].image_path())



     






















    
    
Out[8]:







      
ndarray (300, 299, 3) 
array([[[67, 56, 50],
        [62, 51, 45],
        [56, 45, 39],
        ...,
        [22, 19, 12],
        [22, 19, 12],
        [23, 18, 12]],

       [[65, 54, 48],
        [64, 53, 47],
        [56, 45, 39],
        ...,
        [22, 19, 12],
        [22, 19, 12],
        [23, 18, 12]],

       [[61, 50, 46],
        [59, 48, 44],
        [47, 36, 30],
        ...,
        [23, 18, 12],
        [23, 18, 12],
        [23, 18, 12]],

       ...,

       [[60, 40, 41],
        [60, 40, 41],
        [56, 36, 37],
        ...,
        [30, 21, 16],
        [31, 22, 17],
        [31, 22, 17]],

       [[60, 40, 41],
        [60, 40, 41],
        [57, 37, 38],
        ...,
        [30, 21, 16],
        [31, 22, 17],
        [31, 22, 17]],

       [[61, 41, 42],
        [60, 40, 41],
        [57, 37, 38],
        ...,
        [30, 21, 16],
        [31, 22, 17],
        [31, 22, 17]]], dtype=uint8)




























Part 2.1 d - Generate Embeddings vectors on the
each face in the dataset.



















In [9]:



     


def vgg_face():
    model = Sequential()
    model.add(ZeroPadding2D((1,1),input_shape=(224,224, 3)))
    model.add(Convolution2D(64, (3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(128, (3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(128, (3, 3), activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(256, (3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(256, (3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(256, (3, 3), activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, (3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, (3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, (3, 3), activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, (3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, (3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, (3, 3), activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(Convolution2D(4096, (7, 7), activation='relu'))
    model.add(Dropout(0.5))
    model.add(Convolution2D(4096, (1, 1), activation='relu'))
    model.add(Dropout(0.5))
    model.add(Convolution2D(2622, (1, 1)))
    model.add(Flatten())
    model.add(Activation('softmax'))
    return model



     



















In [10]:



     


model = vgg_face()
model.load_weights('/content/drive/MyDrive/Greatlearning/ComputerVsion Project/vgg_face_weights.h5')



     



















In [11]:



     


vgg_face_descriptor = Model(inputs=model.layers[0].input, outputs=model.layers[-2].output)



     



















In [12]:



     


img_path = metadata[0].image_path()
img = load_image(img_path)

img = (img / 255.).astype(np.float32)

img = cv2.resize(img, dsize = (224,224))
print(img.shape)

embedding_vector = vgg_face_descriptor.predict(np.expand_dims(img, axis=0))[0]
print(embedding_vector.shape)



     






















    
    






(224, 224, 3)
1/1 [==============================] - 3s 3s/step
(2622,)
























In [13]:



     


embeddings = np.zeros((metadata.shape[0], 2622))

for i, m in enumerate(metadata):
      img = load_image(m.image_path())
      img = cv2.resize(img, dsize = (224,224))
      img = (img / 255.).astype(np.float32)
      embeddings[i] = vgg_face_descriptor.predict(np.expand_dims(img, axis=0))[0]



     






















    
    






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Part 2.1 e - Build distance metrics for identifying the
distance between two similar and dissimilar
images.



















In [14]:



     


def distance(emb1, emb2):
    return np.sum(np.square(emb1 - emb2))



     



















In [15]:



     


def show_pair(idx1, idx2):
    plt.figure(figsize=(8,3))
    plt.suptitle(f'Distance = {distance(embeddings[idx1], embeddings[idx2]):.2f}')
    plt.subplot(121)
    plt.imshow(load_image(metadata[idx1].image_path()))
    plt.subplot(122)
    plt.imshow(load_image(metadata[idx2].image_path()));

show_pair(2, 3)
show_pair(2, 180)



     






















    
    

















    
    



































Part 2.1 f - Use PCA for dimensionality reduction.



















In [60]:



     


train_idx = np.arange(metadata.shape[0]) % 9 != 0
test_idx = np.arange(metadata.shape[0]) % 9 == 0

# one half as train examples of 10 identities
X_train = embeddings[train_idx]
# another half as test examples of 10 identities
X_test = embeddings[test_idx]

targets = np.array([m.name for m in metadata])
y_train = targets[train_idx]
y_test = targets[test_idx]



     























Encoding



















In [61]:



     


encoder = LabelEncoder()
y_train = encoder.fit_transform(y_train)
y_test = encoder.transform(y_test)



     























Scaling



















In [62]:



     


sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)



     























PCA - dimensonality reduction



















In [63]:



     


pca = PCA(n_components=128, svd_solver='randomized', whiten=True)
X_train = pca.fit_transform(X_train)
X_test = pca.transform(X_test)



     























Part 2.1 g - Build an SVM classifier in order to map
each image to its right person.



















In [144]:



     


# clf = SVC(kernel='rbf', class_weight=None , C=10, gamma='auto')
# clf.fit(X_train, y_train)
# clf.score(X_test, y_test)

clf = SVC()
param_grid = {
    'C': [0.1, 1, 10],
    'gamma': ['scale', 'auto'],
    'kernel': ['linear', 'rbf']
}
grid_search = GridSearchCV(clf, param_grid, cv=5)
grid_search.fit(X_train, y_train)
print("Best parameters:", grid_search.best_params_)
print("Best score:", grid_search.best_score_)
best_clf = grid_search.best_estimator_
best_clf.fit(X_train, y_train)



     






















    
    






Best parameters: {'C': 1, 'gamma': 'scale', 'kernel': 'rbf'}
Best score: 0.9679420889348499










    
    
Out[144]:







SVC(C=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
SVC(C=1)




























Part 2.1 h - Import and display the the test images.



















In [154]:



     


plt.figure(figsize=(8,3))
plt.subplot(121)
plt.imshow(Image.open("/content/drive/MyDrive/Greatlearning/ComputerVsion Project/testing_images/Benedict Cumberbatch9.jpg"))
plt.subplot(122)
plt.imshow(Image.open("/content/drive/MyDrive/Greatlearning/ComputerVsion Project/testing_images/Dwayne Johnson4.jpg"))



     






















    
    
Out[154]:








<matplotlib.image.AxesImage at 0x7aa884472c20>










    
    



































Part 2.1 i - Use the trained SVM model to predict the
face on both test images.



















In [155]:



     


test_image_directory = "/content/drive/MyDrive/Greatlearning/ComputerVsion Project/testing_images"



     



















In [157]:



     


test_image_filenames = [f for f in os.listdir(test_image_directory) if f.lower().endswith(('png', 'jpg', 'jpeg'))]



     








































In [158]:



     


test_image_filenames



     






















    
    
Out[158]:








['Dwayne Johnson4.jpg', 'Benedict Cumberbatch9.jpg']
























In [159]:



     


test_embeddings = np.zeros((len(test_image_filenames), 2622))
# test_embeddings = []

for i, filename in enumerate(test_image_filenames):
    image_path = os.path.join(test_image_directory, filename)

    img = cv2.imread(image_path)
    img = cv2.resize(img, (224, 224))
    img = (img / 255.).astype(np.float32)

    img_expanded = np.expand_dims(img, axis=0)
    test_embeddings[i] = vgg_face_descriptor.predict(img_expanded)[0]
    print(test_embeddings[i])

print(f"Extracted embeddings for {len(test_embeddings)} images with shape: {test_embeddings.shape}")



     






















    
    






1/1 [==============================] - 0s 22ms/step
[ 0.01430988  0.00752764  0.0158792  ... -0.00922866  0.01744327
  0.00473036]
1/1 [==============================] - 0s 18ms/step
[ 0.0107411  -0.00820719  0.00615904 ... -0.00857323 -0.00022601
  0.0109814 ]
Extracted embeddings for 2 images with shape: (2, 2622)
























In [160]:



     


test_embeddings = sc.transform(test_embeddings)
test_embeddings = pca.transform(test_embeddings)



     



















In [161]:



     


image = load_image(os.path.join(test_image_directory, "Dwayne Johnson4.jpg"))
prediction = best_clf.predict([test_embeddings[0]])
identity = encoder.inverse_transform(prediction)[0]

plt.imshow(image)
plt.title(f'Identified as {identity}')



     






















    
    
Out[161]:








Text(0.5, 1.0, 'Identified as pins_Chris Pratt')










    
    































In [163]:



     


image = load_image(os.path.join(test_image_directory, "Benedict Cumberbatch9.jpg"))
prediction = best_clf.predict([test_embeddings[1]])
print(prediction)
identity = encoder.inverse_transform(prediction)[0]
plt.imshow(image)
plt.title(f'Identified as {identity}')



     






















    
    






[8]










    
    
Out[163]:








Text(0.5, 1.0, 'Identified as pins_Chris Pratt')